We now apply our methods to data from the Framingham Heart Study. Our analysis follows [1]}, where we use a logistic regression model to estimate the impact of age, smoking status, serum cholesterol, and long-term systolic blood pressure on the likelihood of developing coronary heart disease. The data consist of 1615 men, aged 31–65, and come from three examinations of these individuals, over the course of four years. We take the patients' smoking status at the first examination, their age at the second examination, and their cholesterol level at the third examination as error-free covariates. There were two separate measurements of blood pressure taken at each of examination two and examination three. These constitute the error-prone proxies under study. Following [2]} and [3]} we transform the blood pressure measurements to be included in the model as \(\log (\text{SBP} - 50)\) .
